J. Siaw, G. Warnecke, P. Jain, C. Kenney, D. Gershman, R. Riedi, K. Ensor

1. 7 Research questions • What makes the price • Identify orders impacting the price • Effect of order attributes • Volume • Timing • What drives the market • Depending on the “state” of the market, what are the order dynamics • Identify trader “states” (motivations) • Effect of exogenous events • Incorporation of additional microstructure information into existing models Challenges 2 • Large / Complex Data sets: Innovative Data processing required • Extremely large files • Stocks accumulated in same file • Impossible to use traditional software • Existing Market Models outdated? • Statistical analysis required • Ultra – High data frequency • Possibility to cancel order -> Intent unclear: • actual trade vs • influencing the market • Not all orders lead to price changes • Previously unseen microstructure detail 3 Exploratory Data Analysis • Summary statistics • high volume and liquidity • 125 Million book entries per day • 85% of orders placed are cancelled • orders cancelled within the second • volume steadily increasing over years • Features of interest • spread (recall absence of market maker) • survival times of orders (recall the short life span of the majority) Dynamics of Electronic Markets J. Siaw, G. Warnecke, P. Jain, C. Kenney, D. Gershman, R. Riedi, K. Ensor 1 Electronic Markets 4 6 Intraday dynamics Survival of Orders Heat Plot of Sell Orders • Electronic markets (ECNs) are networks that enable users to place orders for stocks via the internet to a system that executes trades automatically (Ex: Island, ArcaBook) • Difference to Traditional markets: • Prices determined by users (no market maker) • Speed Computers used to place orders as well as for Automated Trading • Research needed: the dynamics of ECNs are little studied yet make a large share of trades • Survival Time – time between the addition and removal of an order from the queue • Hazard rate - instantaneous probability that the order will be traded or cancelled during the next instant (before t+t) • Survival rate - probability an added order will survive beyond a certain time • Competing risks – orders may be fully or partially traded or cancelled • Issue of interest: mixture of risk and complexity of data require new models • 3 trading periods per day: • Pre-market (7 AM – 9:30 AM) • Market trading (9:30 AM – 4 PM) • After hours (4 PM – 7 PM) • Pre-market and after hours trading is very sparse Example day: • Pre-market: 79 trades • Market trading: 33961 trades • After hours: 4091 trades • Trading activity correlates with activity on order book • Main period of interest: Market trading • Issue of interest: Stationarity Heat Plot of Sell Orders Heat Plot of Buy Orders Spread 5 • price –price of last trade (unique to stock) • Limit Orders: queued to be matched or cancelled • best bid - the highest buy order in the queue • best ask – the lowest sell order in the queue • spread = (best ask) – (best bid) • Highly traded stocks (ex. Microsoft): • spread usually $0.01; deviates only shortly • Less traded stocks: spread usually larger • Issues of interest: • absence of market maker shows in 1c-spread • requires new models 8 Future Work • Survival analysis • Competing risk models • Cox proportional hazard model • Self-excitation (ACD models) • Spread • Correlation • ARIMA time series • Self-excitation • Conditional / Hidden parameter model • Arbitrage • Sensitivity to networks